Internal Organ Localization Using Depth Images

Eytan Kats, Kai Geißler, Jochen G. Hirsch, Stefan Heldman, Mattias P. Heinrich
3/30/2025
cs.CV

No official code found. Best implementation: EytanKats/orgloc (0 stars, MIT, last commit 2026-03-14).

Abstract

Automated patient positioning is a crucial step in streamlining MRI workflows and enhancing patient throughput. RGB-D camera-based systems offer a promising approach to automate this process by leveraging depth information to estimate internal organ positions. This paper investigates the feasibility of a learning-based framework to infer approximate internal organ positions from the body surface. Our approach utilizes a large-scale dataset of MRI scans to train a deep learning model capable of accurately predicting organ positions and shapes from depth images alone. We demonstrate the effectiveness of our method in localization of multiple internal organs, including bones and soft tissues. Our findings suggest that RGB-D camera-based systems integrated into MRI workflows have the potential to streamline scanning procedures and improve patient experience by enabling accurate and automated patient positioning.

View on arXivView PDF

Code Implementations(1)

EytanKats/orglocCommunity73%
MIT

Internal organ localization using depth images

00Python, Jupyter NotebookMar 17, 20257 months agoMIT

Similar Papers

Depth to Anatomy: Learning Internal Organ Locations from Surface Depth Images

92% match2026

LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images

86% match2024

MASSM: An End-to-End Deep Learning Framework for Multi-Anatomy Statistical Shape Modeling Directly From Images

84% match2024

Vision-Based Neurosurgical Guidance: Unsupervised Localization and Camera-Pose Prediction

84% match2024

Pose Estimation for Intra-cardiac Echocardiography Catheter via AI-Based Anatomical Understanding

83% match2025

Anatomical Positional Embeddings

83% match1 repo52024

Cite this paper

@article{kats2025internal,
  title  = {Internal Organ Localization Using Depth Images},
  author = {Eytan Kats and Kai Geißler and Jochen G. Hirsch and Stefan Heldman and Mattias P. Heinrich},
  year   = {2025},
  eprint = {2503.23468},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2503.23468v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2503.23468.svg)](https://codeofpaper.com/paper/2503.23468)

Discussion